Multi-Model Federated Learning-Reference-Cited by-同舟云学术

Multi-Model Federated Learning

Published:2022-01-04 Issue: Volume: Page:
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Container-title:2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS)
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Author:

Bhuyan Neelkamal,Moharir Sharayu

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9668311/9667790/09668435.pdf?arnumber=9668435

Reference18 articles.

1. Optimal client sampling for federated learning;chen;ArXiv Preprint,2020

2. Multi-Armed Bandit-Based Client Scheduling for Federated Learning

3. Bandit-based communication-efficient client selection strategies for federated learning;cho;2020 54th Asilomar Conference on Signals Systems and Computers,0

4. Leaf: A benchmark for federated settings;caldas;ArXiv Preprint,2018

5. Designing multi-objective multi-armed bandits algorithms: A study

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4. Sowing the Seeds of AI in Agriculture: Federated Learning CNN for Groundnut Disease Detection;2023 4th IEEE Global Conference for Advancement in Technology (GCAT);2023-10-06

5. Apple Leaf Disease Recognition: A Robust Federated Learning CNN Methodology;2023 International Conference on Circuit Power and Computing Technologies (ICCPCT);2023-08-10